Filter out rare taxa and those not classified as bacteria or archaea

Exploring dataset features

First let’s look at the count data distribution

I will test now the effect of library size and all other experimental factors on the community composition and also plot

## 
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP_merged), method = "bray") ~      Site * Oxygen * Hours + Lib.size, data = as(sample_data(Ps_obj_SIP_merged),      "data.frame"), permutations = 999) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                    Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Site                1    22.555 22.5553 314.924 0.39126  0.001 ***
## Oxygen              1     6.377  6.3767  89.033 0.11061  0.001 ***
## Hours               1     0.333  0.3332   4.652 0.00578  0.002 ** 
## Lib.size            1     4.943  4.9430  69.016 0.08575  0.001 ***
## Site:Oxygen         1     1.483  1.4830  20.706 0.02573  0.001 ***
## Site:Hours          1     0.471  0.4707   6.572 0.00817  0.002 ** 
## Oxygen:Hours        1     0.281  0.2809   3.922 0.00487  0.005 ** 
## Site:Oxygen:Hours   1     0.363  0.3631   5.070 0.00630  0.001 ***
## Residuals         291    20.842  0.0716         0.36154           
## Total             299    57.648                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Modelling library size shows a significant effect of read depth on the community structure, but explaining only 9% of the variance. The reads histogram shows as expected a highly sparse and skewed sequence matrix. The mean vs SD also shows as expected large dependency of SD on the mean reads of a sequence across all samples.

Taxa-based filtering

Frist let’s look at the taxonomic distribution

## 
##  Archaea Bacteria 
##      185    14280
## 
##          Abditibacteria          Acidimicrobiia          Acidobacteriae 
##                      21                     679                    1199 
##          Actinobacteria                     AD3     Alphaproteobacteria 
##                     784                      27                    2031 
##            Anaerolineae           Armatimonadia                Babeliae 
##                       3                      84                     680 
##                 Bacilli             Bacteroidia                  BD7-11 
##                     459                     503                      16 
##         Bdellovibrionia          Blastocatellia         Campylobacteria 
##                     110                      20                       5 
##              Chlamydiae            Chloroflexia        Chthonomonadetes 
##                     583                       4                      63 
##              Clostridia          Coriobacteriia          Cyanobacteriia 
##                     276                       4                     178 
##         Dehalococcoidia              Deinococci      Desulfitobacteriia 
##                       1                      10                      12 
##           Desulfobulbia        Desulfovibrionia           Elusimicrobia 
##                       1                       2                      14 
##            Endomicrobia           Fibrobacteria          Fimbriimonadia 
##                       2                       5                      58 
##           Fusobacteriia     Gammaproteobacteria        Gemmatimonadetes 
##                      21                    2250                      25 
##             Gitt-GS-136         Gracilibacteria            Halobacteria 
##                       1                       1                       1 
##              Holophagae         Hydrogenedentia            JG30-KF-CM66 
##                       2                       1                      25 
##            Kapabacteria                  KD4-96         Ktedonobacteria 
##                      10                       1                      82 
##           Lentisphaeria             Lineage IIa           Longimicrobia 
##                       2                       5                       2 
##         Methanobacteria            Micrarchaeia              Myxococcia 
##                       1                       4                     111 
##           Negativicutes         Nitrososphaeria             Oligoflexia 
##                      13                     129                     106 
##                   OM190             Omnitrophia           Parcubacteria 
##                       1                      11                      11 
##           Phycisphaerae          Planctomycetes               Polyangia 
##                     154                    1703                     146 
##            Rhodothermia S0134 terrestrial group         Saccharimonadia 
##                       2                       1                      49 
##       Sericytochromatia                  SJA-28            Spirochaetia 
##                       1                       1                       1 
##             Subgroup 22              Subgroup 5       Syntrophobacteria 
##                       1                      12                       1 
##     Thermoanaerobaculia         Thermoleophilia          Thermoplasmata 
##                       2                     223                      50 
##                    TK10            Unclassified               vadinHA49 
##                      10                     615                      11 
##        Vampirivibrionia        Verrucomicrobiae        Vicinamibacteria 
##                     206                     599                      27

Now let’s remove some taxa which are obvious artefacts or those which aren’t bacteria or archaea

Level ASVs.removed Seqs.removed
Kingdom 0 0
Order 136 4294
Family 138 15725

Removed 0.0335% of the sequences.

Now let’s explore the prevalence of different taxa in the database. Prevalence is the number of samples in which a taxa appears at least once. So “Mean prevalence” refers to in how many samples does a sequence belonging to the phylum appears on average, and “Sum prevalence” is the sum of all samples where any sequence from the taxon appears.

Phylum Mean prevalence Sum prevalence
Abditibacteriota 21.3 448
Acidobacteriota 58.2 73619
Actinobacteriota 51.3 86754
Armatimonadota 43.5 9178
Bacteroidota 20.9 10783
Bdellovibrionota 7.5 1625
Campilobacterota 3.6 18
Chloroflexi 29.5 4632
Crenarchaeota 38.9 5012
Cyanobacteria 17.5 4458
Deinococcota 1.5 15
Dependentiae 11.7 7981
Desulfobacterota 6.7 40
Elusimicrobiota 13.0 273
Euryarchaeota 1.0 1
FCPU426 44.6 1606
Fibrobacterota 9.0 45
Firmicutes 22.1 18329
Fusobacteriota 4.4 93
GAL15 40.1 281
Gemmatimonadota 16.7 468
Halobacterota 1.0 1
Hydrogenedentes 1.0 1
Micrarchaeota 1.5 6
Myxococcota 46.7 11996
Patescibacteria 9.0 588
Planctomycetota 36.0 67790
Proteobacteria 28.2 119347
RCP2-54 105.0 8924
SAR324 clade(Marine group B) 33.0 33
Spirochaetota 1.0 1
Thermoplasmatota 49.3 2464
Unclassified 10.3 1825
Verrucomicrobiota 29.7 35502
WPS-2 63.1 8456
Order Mean prevalence Sum prevalence
0319-6G20 8.2 655
11-24 7.5 15
Abditibacteriales 21.3 448
Absconditabacteriales (SR1) 2.0 2
Acetobacterales 69.1 16435
Acholeplasmatales 1.0 1
Acidaminococcales 1.0 1
Acidimicrobiales 1.0 1
Acidobacteriales 51.6 30887
Actinomycetales 3.5 38
Aeromonadales 1.0 540
Alicyclobacillales 26.6 186
Alteromonadales 1.0 1
Ardenticatenales 1.0 2
Armatimonadales 43.8 3676
Azospirillales 38.2 992
B12-WMSP1 1.0 1
Babeliales 11.7 7981
Bacillales 35.0 3151
Bacteriovoracales 1.2 5
Bacteroidales 2.0 117
Bacteroidetes VC2.1 Bac22 1.5 3
Bdellovibrionales 6.5 687
Bifidobacteriales 1.0 3
Blastocatellales 1.3 22
Blfdi19 22.5 45
Bryobacterales 76.7 11734
Burkholderiales 23.8 6741
Caedibacterales 11.7 82
Caldalkalibacillales 3.5 7
Campylobacterales 3.6 18
Candidatus Jorgensenbacteria 3.8 23
Candidatus Kaiserbacteria 1.0 1
Cardiobacteriales 4.3 13
Catenulisporales 97.8 2151
Caulobacterales 57.1 9421
Cellvibrionales 1.0 2
Chitinophagales 34.7 5136
Chlamydiales 17.1 9989
Chloroflexales 1.0 1
Christensenellales 1.4 18
Chthoniobacterales 26.0 4752
Chthonomonadales 19.0 1199
Clostridia UCG-014 1.0 2
Clostridiales 16.8 606
Coriobacteriales 1.2 5
Corynebacteriales 41.8 5059
Coxiellales 13.5 3232
Cyanobacteriales 3.9 126
Cytophagales 5.1 471
Deinococcales 1.5 15
Desulfitobacteriales 23.2 279
Desulfobulbales 1.0 1
Desulfovibrionales 1.0 2
Diplorickettsiales 10.1 2318
Dongiales 2.0 2
Elev-1554 14.5 29
Elev-16S-1166 1.0 1
Elsterales 57.4 19463
Endomicrobiales 1.0 2
Enterobacterales 4.5 150
Entomoplasmatales 8.2 41
Erysipelotrichales 6.7 100
Exiguobacterales 1.0 3
FCPU453 32.5 130
Fibrobacterales 9.0 45
Fimbriimonadales 70.2 4072
Flavobacteriales 2.6 124
Frankiales 83.3 28396
Fusobacteriales 4.4 93
Gaiellales 71.8 933
Gammaproteobacteria Incertae Sedis 32.0 6088
Gastranaerophilales 1.0 3
Gemmatales 34.4 49765
Gemmatimonadales 18.6 465
Group 1.1c 39.2 3335
Haliangiales 37.5 488
Halobacterales 1.0 1
Holosporales 22.9 1280
Hungateiclostridiaceae 1.0 2
Hydrogenedentiales 1.0 1
I3A 10.6 53
IMCC26256 37.0 7954
Isosphaerales 55.7 5624
JG36-TzT-191 82.3 3375
Kapabacteriales 38.3 383
KF-JG30-C25 20.0 60
Kineosporiales 1.0 5
Ktedonobacterales 27.6 2232
Lachnospirales 5.6 767
Lactobacillales 14.4 1209
Legionellales 19.7 3175
Leptolyngbyales 1.0 1
Lineage IV 10.9 98
Longimicrobiales 1.0 2
Methanobacteriales 1.0 1
Methanomassiliicoccales 1.0 2
Methylacidiphilales 33.8 2163
Methylococcales 9.0 18
Micavibrionales 57.0 57
Micrarchaeales 1.5 6
Micrococcales 7.4 891
Micromonosporales 1.5 3
Micropepsales 56.0 4645
Microtrichales 2.1 27
mle1-27 11.0 44
Monoglobales 1.0 4
Myxococcales 22.2 2464
Nitrosococcales 37.0 74
Nitrososphaerales 60.5 1150
Nitrosotaleales 25.6 436
Obscuribacterales 21.2 4153
Oceanospirillales 2.7 8
Oligoflexales 16.6 216
Omnitrophales 25.5 281
Opitutales 38.4 3957
Oscillospirales 4.6 146
Oxyphotobacteria Incertae Sedis 1.0 2
Paenibacillales 53.1 8012
Paracaedibacterales 11.6 792
Pasteurellales 14.1 127
Pedosphaerales 70.4 13805
Peptococcales 1.0 1
Peptostreptococcales-Tissierellales 6.4 256
Phormidesmiales 1.0 2
Phycisphaerales 21.0 524
Pirellulales 34.5 4455
Planctomycetales 85.8 2230
Polyangiales 70.8 8783
Propionibacteriales 10.5 221
Pseudanabaenales 1.0 1
Pseudomonadales 15.1 1782
Pseudonocardiales 36.8 699
Pyrinomonadales 1.0 1
Reyranellales 29.3 352
Rhizobiales 62.3 23562
Rhodobacterales 2.6 84
Rhodospirillales 13.8 1658
Rhodothermales 1.0 2
Rickettsiales 15.6 1485
S-BQ2-57 soil group 11.8 177
S085 1.0 1
Saccharimonadales 11.0 537
Salinisphaerales 48.2 1013
SAR11 clade 1.0 1
SBR1031 1.0 1
Silvanigrellales 4.8 62
Solibacterales 89.8 10771
Solirubrobacterales 75.9 15946
Sphingobacteriales 29.4 4468
Sphingomonadales 4.6 705
Spirochaetales 1.0 1
Staphylococcales 21.6 410
Steroidobacterales 1.0 1
Streptomycetales 63.1 883
Streptosporangiales 2.9 105
Subgroup 12 10.8 43
Subgroup 13 61.6 1725
Subgroup 15 5.1 72
Subgroup 2 73.8 16985
Subgroup 7 1.0 2
Synechococcales 1.0 2
Syntrophobacterales 35.0 35
Tepidisphaerales 35.1 4533
Thalassobaculales 1.0 1
Thermicanales 5.0 10
Thermoactinomycetales 30.0 1951
Thermoanaerobaculales 15.5 31
Thermomicrobiales 1.0 1
TSBb06 10.2 61
Unclassified 31.5 56145
Vampirovibrionales 17.3 104
Veillonellales-Selenomonadales 6.7 80
Verrucomicrobiales 10.1 375
Vibrionales 1.7 5
Vicinamibacterales 31.3 846
Victivallales 1.0 2
WD260 158.8 3335
Xanthomonadales 29.6 2336

Based on that I’ll remove all orders with a sum prevalence of under 5% (15) of all samples

## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 14199 taxa and 300 samples ]:
## sample_data() Sample Data:        [ 300 samples by 29 sample variables ]:
## tax_table()   Taxonomy Table:     [ 14199 taxa by 6 taxonomic ranks ]:
## taxa are columns
## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 14099 taxa and 300 samples ]:
## sample_data() Sample Data:        [ 300 samples by 29 sample variables ]:
## tax_table()   Taxonomy Table:     [ 14099 taxa by 6 taxonomic ranks ]:
## taxa are columns

This removed 100 or 1% of the ESVs, and 0.046% of the reads.

Plot general prevalence features of the phyla

Plot general prevalence features of the top 20 orders

Unsupervised filtering by prevalence

I’ll remove all sequences which appear in less than 5% of the samples

## [1] 30
## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 14099 taxa and 300 samples ]:
## sample_data() Sample Data:        [ 300 samples by 29 sample variables ]:
## tax_table()   Taxonomy Table:     [ 14099 taxa by 6 taxonomic ranks ]:
## taxa are columns
## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 3693 taxa and 300 samples ]:
## sample_data() Sample Data:        [ 300 samples by 29 sample variables ]:
## tax_table()   Taxonomy Table:     [ 3693 taxa by 6 taxonomic ranks ]:
## taxa are columns

This removed 10406 or 74% of the ESVs!

However all these removed ESVs accounted for only:

Prevalence > prevalenceThreshold Abundance Rel. Ab.
FALSE 303010 2.37%
TRUE 12498417 97.63%

So it’s fine to remove them.

Test again the effect of library size and all other experimental factors on the community composition after filtering

## 
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP_merged_filt3),      method = "bray") ~ Site * Oxygen * Hours + Lib.size, data = as(sample_data(Ps_obj_SIP_merged_filt3),      "data.frame"), permutations = 999) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                    Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Site                1    22.638 22.6379  342.15 0.40294  0.001 ***
## Oxygen              1     6.358  6.3575   96.09 0.11316  0.001 ***
## Hours               1     0.322  0.3223    4.87 0.00574  0.005 ** 
## Lib.size            1     5.069  5.0686   76.61 0.09022  0.001 ***
## Site:Oxygen         1     1.475  1.4747   22.29 0.02625  0.001 ***
## Site:Hours          1     0.458  0.4576    6.92 0.00814  0.001 ***
## Oxygen:Hours        1     0.264  0.2643    3.99 0.00470  0.007 ** 
## Site:Oxygen:Hours   1     0.345  0.3448    5.21 0.00614  0.002 ** 
## Residuals         291    19.254  0.0662         0.34270           
## Total             299    56.181                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Save filtered phyloseq object

Current session info


─ Session info ─────────────────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.3 (2020-10-10)
 os       Ubuntu 18.04.5 LTS          
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       Europe/Prague               
 date     2021-02-11                  

─ Packages ─────────────────────────────────────────────────────────────────────────────
 package        * version    date       lib source                           
 ade4             1.7-16     2020-10-28 [1] CRAN (R 4.0.2)                   
 affy             1.66.0     2020-04-27 [1] Bioconductor                     
 affyio           1.58.0     2020-04-27 [1] Bioconductor                     
 ape              5.4-1      2020-08-13 [1] CRAN (R 4.0.2)                   
 assertthat       0.2.1      2019-03-21 [1] CRAN (R 4.0.2)                   
 backports        1.2.1      2020-12-09 [1] CRAN (R 4.0.2)                   
 bayestestR       0.8.2      2021-01-26 [1] CRAN (R 4.0.3)                   
 Biobase        * 2.48.0     2020-04-27 [1] Bioconductor                     
 BiocGenerics   * 0.34.0     2020-04-27 [1] Bioconductor                     
 BiocManager      1.30.10    2019-11-16 [1] CRAN (R 4.0.2)                   
 biomformat       1.16.0     2020-04-27 [1] Bioconductor                     
 Biostrings     * 2.56.0     2020-04-27 [1] Bioconductor                     
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 cli              2.3.0      2021-01-31 [1] CRAN (R 4.0.3)                   
 clipr            0.7.1      2020-10-08 [1] CRAN (R 4.0.2)                   
 cluster          2.1.0      2019-06-19 [1] CRAN (R 4.0.2)                   
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 foreach          1.5.1      2020-10-15 [1] CRAN (R 4.0.2)                   
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 glue             1.4.2      2020-08-27 [1] CRAN (R 4.0.2)                   
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 haven            2.3.1      2020-06-01 [1] CRAN (R 4.0.2)                   
 hexbin           1.28.2     2021-01-08 [1] CRAN (R 4.0.2)                   
 highr            0.8        2019-03-20 [1] CRAN (R 4.0.2)                   
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 htmltools        0.5.1.1    2021-01-22 [1] CRAN (R 4.0.3)                   
 httr             1.4.2      2020-07-20 [1] CRAN (R 4.0.2)                   
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 knitr            1.31       2021-01-27 [1] CRAN (R 4.0.3)                   
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 lattice        * 0.20-41    2020-04-02 [1] CRAN (R 4.0.2)                   
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 limma            3.44.3     2020-06-12 [1] Bioconductor                     
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 magrittr       * 2.0.1      2020-11-17 [1] CRAN (R 4.0.2)                   
 MASS             7.3-53     2020-09-09 [1] CRAN (R 4.0.2)                   
 Matrix           1.3-2      2021-01-06 [1] CRAN (R 4.0.2)                   
 mgcv             1.8-33     2020-08-27 [1] CRAN (R 4.0.2)                   
 modelr           0.1.8      2020-05-19 [1] CRAN (R 4.0.2)                   
 multtest         2.44.0     2020-04-27 [1] Bioconductor                     
 munsell          0.5.0      2018-06-12 [1] CRAN (R 4.0.2)                   
 nlme             3.1-152    2021-02-04 [1] CRAN (R 4.0.3)                   
 parameters       0.11.0     2021-01-15 [1] CRAN (R 4.0.3)                   
 permute        * 0.9-5      2019-03-12 [1] CRAN (R 4.0.2)                   
 phyloseq       * 1.32.0     2020-04-27 [1] Bioconductor                     
 pillar           1.4.7      2020-11-20 [1] CRAN (R 4.0.2)                   
 pkgconfig        2.0.3      2019-09-22 [1] CRAN (R 4.0.2)                   
 plyr             1.8.6      2020-03-03 [1] CRAN (R 4.0.2)                   
 png              0.1-7      2013-12-03 [1] CRAN (R 4.0.2)                   
 preprocessCore   1.50.0     2020-04-27 [1] Bioconductor                     
 prettyunits      1.1.1      2020-01-24 [1] CRAN (R 4.0.2)                   
 progress         1.2.2      2019-05-16 [1] CRAN (R 4.0.2)                   
 purrr          * 0.3.4      2020-04-17 [1] CRAN (R 4.0.2)                   
 R6               2.5.0      2020-10-28 [1] CRAN (R 4.0.2)                   
 RColorBrewer     1.1-2      2014-12-07 [1] CRAN (R 4.0.2)                   
 Rcpp             1.0.6      2021-01-15 [1] CRAN (R 4.0.3)                   
 readr          * 1.4.0      2020-10-05 [1] CRAN (R 4.0.2)                   
 readxl           1.3.1      2019-03-13 [1] CRAN (R 4.0.2)                   
 reprex           1.0.0      2021-01-27 [1] CRAN (R 4.0.3)                   
 reshape2         1.4.4      2020-04-09 [1] CRAN (R 4.0.2)                   
 rhdf5            2.32.4     2020-10-05 [1] Bioconductor                     
 Rhdf5lib         1.10.1     2020-07-09 [1] Bioconductor                     
 rlang            0.4.10     2020-12-30 [1] CRAN (R 4.0.2)                   
 rmarkdown        2.6        2020-12-14 [1] CRAN (R 4.0.2)                   
 rprojroot        2.0.2      2020-11-15 [1] CRAN (R 4.0.2)                   
 rstudioapi       0.13       2020-11-12 [1] CRAN (R 4.0.2)                   
 Rttf2pt1         1.3.8      2020-01-10 [1] CRAN (R 4.0.2)                   
 rvest            0.3.6      2020-07-25 [1] CRAN (R 4.0.2)                   
 S4Vectors      * 0.26.1     2020-05-16 [1] Bioconductor                     
 scales         * 1.1.1      2020-05-11 [1] CRAN (R 4.0.2)                   
 see            * 0.6.2      2021-02-04 [1] CRAN (R 4.0.3)                   
 sessioninfo      1.1.1      2018-11-05 [1] CRAN (R 4.0.2)                   
 speedyseq      * 0.5.3.9001 2020-10-27 [1] Github (mikemc/speedyseq@8daed32)
 stringi          1.5.3      2020-09-09 [1] CRAN (R 4.0.2)                   
 stringr        * 1.4.0      2019-02-10 [1] CRAN (R 4.0.2)                   
 survival         3.2-7      2020-09-28 [1] CRAN (R 4.0.2)                   
 svglite        * 1.2.3.2    2020-07-07 [1] CRAN (R 4.0.2)                   
 systemfonts      1.0.1      2021-02-09 [1] CRAN (R 4.0.3)                   
 tibble         * 3.0.6      2021-01-29 [1] CRAN (R 4.0.3)                   
 tidyr          * 1.1.2      2020-08-27 [1] CRAN (R 4.0.2)                   
 tidyselect       1.1.0      2020-05-11 [1] CRAN (R 4.0.2)                   
 tidyverse      * 1.3.0      2019-11-21 [1] CRAN (R 4.0.2)                   
 vctrs            0.3.6      2020-12-17 [1] CRAN (R 4.0.2)                   
 vegan          * 2.5-7      2020-11-28 [1] CRAN (R 4.0.2)                   
 viridisLite      0.3.0      2018-02-01 [1] CRAN (R 4.0.2)                   
 vsn            * 3.56.0     2020-04-27 [1] Bioconductor                     
 webshot          0.5.2      2019-11-22 [1] CRAN (R 4.0.2)                   
 withr            2.4.1      2021-01-26 [1] CRAN (R 4.0.3)                   
 xfun             0.20       2021-01-06 [1] CRAN (R 4.0.2)                   
 xml2             1.3.2      2020-04-23 [1] CRAN (R 4.0.2)                   
 XVector        * 0.28.0     2020-04-27 [1] Bioconductor                     
 yaml             2.2.1      2020-02-01 [1] CRAN (R 4.0.2)                   
 zlibbioc         1.34.0     2020-04-27 [1] Bioconductor                     

[1] /home/angel/R/library
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library


References